Project: | Stanford 2D FSE |
Anatomy: | Lower Extremity |
Fullysampled: | Yes |
Uploader: | jycheng |
Tags: |
UUID | 2d74779c-09c4-4dab-b7e3-83ab7e35be7f |
---|---|
Downloads | 1111 |
Comments | https://github.com/MRSRL/mridata-recon/ UUID:793ffee5-bb60-53ab-9645-f12dff3b82d6_Ser4 |
Funding Support | NIH R01 EB009690 |
Protocol Name | |
Series Description | |
System Vendor | GE MEDICAL SYSTEMS |
System Model | Orchestra SDK |
System Field Strength | 3.0 T |
Receiver Bandwidth | 41.669998 |
Number of Channels | 16 |
Coil Name | C-GE_32Body LT Bone |
Station Name | ANONYMIZED |
Matrix Size | 352 x 174 x 30 |
Field Of View | 350.0 mm x 350.0 mm x 4.5 mm |
Number of Slices | 30 |
Number of Phases | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Repetition Time | 1100 ms |
Echo Time | 8.8 ms |
Flip Angle | 111 ° |
Sequence Type | SE |
Upload Date | Aug. 6, 2018, 10:07 p.m. |
Project: | Stanford 2D FSE |
Anatomy: | Pelvis |
Fullysampled: | Yes |
Uploader: | jycheng |
Tags: |
UUID | 292aa3a9-a287-4883-a272-b2ba2f7ca834 |
---|---|
Downloads | 1111 |
Comments | https://github.com/MRSRL/mridata-recon/ UUID:793ffee5-bb60-53ab-9645-f12dff3b82d6_Ser2 |
Funding Support | NIH R01 EB009690 |
Protocol Name | |
Series Description | |
System Vendor | GE MEDICAL SYSTEMS |
System Model | Orchestra SDK |
System Field Strength | 3.0 T |
Receiver Bandwidth | 41.669998 |
Number of Channels | 32 |
Coil Name | C-GE_32Body Full |
Station Name | ANONYMIZED |
Matrix Size | 352 x 174 x 30 |
Field Of View | 350.0 mm x 350.0 mm x 4.5 mm |
Number of Slices | 30 |
Number of Phases | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Repetition Time | 1100 ms |
Echo Time | 8.8 ms |
Flip Angle | 111 ° |
Sequence Type | SE |
Upload Date | Aug. 6, 2018, 10:06 p.m. |
Project: | Stanford 2D FSE |
Anatomy: | Lower Extremity |
Fullysampled: | Yes |
Uploader: | jycheng |
Tags: |
UUID | f63bc805-31ae-416b-ad60-29fdef5be0a6 |
---|---|
Downloads | 1136 |
Comments | https://github.com/MRSRL/mridata-recon/ UUID:debb6840-e463-5ba0-9707-cdfdd15b3827_Ser6 |
Funding Support | NIH R01 EB009690 |
Protocol Name | |
Series Description | |
System Vendor | GE MEDICAL SYSTEMS |
System Model | Orchestra SDK |
System Field Strength | 3.0 T |
Receiver Bandwidth | 41.669998 |
Number of Channels | 16 |
Coil Name | C-GEM Flex LG Full |
Station Name | ANONYMIZED |
Matrix Size | 320 x 256 x 35 |
Field Of View | 180.0 mm x 180.0 mm x 4 mm |
Number of Slices | 35 |
Number of Phases | 1 |
Number of Contrasts | 2 |
Trajectory | cartesian |
Repetition Time | 4389 ms |
Echo Time | 60 ms |
Flip Angle | 111 ° |
Sequence Type | SE |
Upload Date | Aug. 6, 2018, 10:04 p.m. |
Project: | Stanford 2D FSE |
Anatomy: | Lower Extremity |
Fullysampled: | Yes |
Uploader: | jycheng |
Tags: |
UUID | bddedfa8-eb63-4eca-967c-2abce1c2b0d6 |
---|---|
Downloads | 1078 |
Comments | https://github.com/MRSRL/mridata-recon/ UUID:debb6840-e463-5ba0-9707-cdfdd15b3827_Ser3 |
Funding Support | NIH R01 EB009690 |
Protocol Name | |
Series Description | |
System Vendor | GE MEDICAL SYSTEMS |
System Model | Orchestra SDK |
System Field Strength | 3.0 T |
Receiver Bandwidth | 41.669998 |
Number of Channels | 16 |
Coil Name | C-GEM Flex LG Full |
Station Name | ANONYMIZED |
Matrix Size | 320 x 224 x 23 |
Field Of View | 220.0 mm x 220.0 mm x 4 mm |
Number of Slices | 23 |
Number of Phases | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Repetition Time | 4450 ms |
Echo Time | 30 ms |
Flip Angle | 111 ° |
Sequence Type | RM |
Upload Date | Aug. 6, 2018, 10:02 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | edb3e7ff-463c-433e-94cf-700ed004365e |
---|---|
Downloads | 910 |
References | Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018) |
Comments | This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork. |
Funding Support | NIH P41 EB017183 |
Protocol Name | COR |
Series Description | COR |
System Vendor | SIEMENS |
System Model | Skyra |
System Field Strength | 2.89362 T |
Receiver Bandwidth | 0.793 |
Number of Channels | 15 |
Coil Name | TxRx_15Ch_Knee:1:K5 |
Institution Name | HJD |
Matrix Size | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 44 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 3480 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:45 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 721b2fa6-2500-4b24-be9f-312d9627119e |
---|---|
Downloads | 918 |
References | Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018) |
Comments | This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork. |
Funding Support | NIH P41 EB017183 |
Protocol Name | COR |
Series Description | COR |
System Vendor | SIEMENS |
System Model | Skyra |
System Field Strength | 2.89362 T |
Receiver Bandwidth | 0.793 |
Number of Channels | 15 |
Coil Name | TxRx_15Ch_Knee:1:K5 |
Institution Name | HJD |
Matrix Size | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 36 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 2870 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:44 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 9d4b66ea-e3db-4ea8-b3a8-1e6c2642e6d9 |
---|---|
Downloads | 909 |
References | Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018) |
Comments | This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork. |
Funding Support | NIH P41 EB017183 |
Protocol Name | COR |
Series Description | COR |
System Vendor | SIEMENS |
System Model | Skyra |
System Field Strength | 2.89362 T |
Receiver Bandwidth | 0.793 |
Number of Channels | 15 |
Coil Name | TxRx_15Ch_Knee:1:K5 |
Institution Name | HJD |
Matrix Size | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 37 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 2930 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:43 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | c01e98cd-e697-417e-ba10-ee6ed44cd1fa |
---|---|
Downloads | 931 |
References | Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018) |
Comments | This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork. |
Funding Support | NIH P41 EB017183 |
Protocol Name | COR |
Series Description | COR |
System Vendor | SIEMENS |
System Model | Skyra |
System Field Strength | 2.89362 T |
Receiver Bandwidth | 0.793 |
Number of Channels | 15 |
Coil Name | TxRx_15Ch_Knee:1:K5 |
Institution Name | HJD |
Matrix Size | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 37 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 2930 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:40 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 0e4365a8-a975-437a-95f4-35bcde5da5f1 |
---|---|
Downloads | 936 |
References | Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018) |
Comments | This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork. |
Funding Support | NIH P41 EB017183 |
Protocol Name | COR |
Series Description | COR |
System Vendor | SIEMENS |
System Model | Skyra |
System Field Strength | 2.89362 T |
Receiver Bandwidth | 0.793 |
Number of Channels | 15 |
Coil Name | TxRx_15Ch_Knee:1:K5 |
Institution Name | HJD |
Matrix Size | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 38 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 3010 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:38 p.m. |
Project: | NYU machine learning data |
Anatomy: | Knee |
Fullysampled: | Yes |
Uploader: | florianknoll |
Tags: |
UUID | 3578cd98-650c-4489-884e-7e162202b961 |
---|---|
Downloads | 937 |
References | Hammernik K, Klatzer T, Kobler E, Recht M, Sodickson D, Pock T, Knoll F. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Magnetic Resonance in Medicine 79: 3055–3071 (2018) |
Comments | This is part of the training and test data that was used for our 2017 MRM manuscript on learning a variational network to reconstruct accelerated MR data. The data accompanies the code repository at: https://github.com/VLOGroup/mri-variationalnetwork. |
Funding Support | NIH P41 EB017183 |
Protocol Name | COR |
Series Description | COR |
System Vendor | SIEMENS |
System Model | Skyra |
System Field Strength | 2.89362 T |
Receiver Bandwidth | 0.793 |
Number of Channels | 15 |
Coil Name | TxRx_15Ch_Knee:1:K5 |
Institution Name | HJD |
Matrix Size | 640 x 368 x 1 |
Field Of View | 280 mm x 161.4 mm x 4.5 mm |
Number of Averages | 1 |
Number of Slices | 34 |
Number of Phases | 1 |
Number of Repetition | 1 |
Number of Contrasts | 1 |
Trajectory | cartesian |
Parallel Imaging Factor | 1.0 x 1.0 |
Repetition Time | 2870 ms |
Echo Time | 33 ms |
Inversion Time | 100 ms |
Flip Angle | 180 ° |
Sequence Type | TurboSpinEcho |
Echo Spacing | 10.96 ms |
Upload Date | Aug. 6, 2018, 12:37 p.m. |